The next generation of health information technology, organized as “learning health systems,” promises efficient, engineered solutions to the well-known and enduring maladies of the existing U.S. health infrastructure: escalating costs, poor health outcomes, ineffective use of technology, sluggish research pipelines, dangerous medical error rates, and failure to implement known clinical best practices. Learning health systems would capitalize on "big data" enterprises to accelerate the production and application of knowledge in health care. However, the sharing of health information required, both within and across institutions, greatly exceeds the public’s understanding. These initiatives are riding a precarious edge as the gap between public expectations and the realities of institutional data sharing widens at an unprecedented rate. This presentation considers the causes and consequences of trust and mistrust of health information systems, their data sharing practices, and their policy implications.